A latent variable model for identifying and characterizing food adulteration

2025-12-15

AI Summary

To address challenges in food adulteration detection—including low discrimination sensitivity, inaccurate quantification, and poor interpretability—this study proposes a latent variable modeling framework tailored for mid-infrared (MIR) vibrational spectroscopy. The method jointly accomplishes three tasks: (1) binary adulteration classification, (2) precise quantification of adulterant concentration (achieving <3% mean absolute error on both synthetic and real honey datasets), and (3) identification of chemically sensitive spectral regions. To our knowledge, this is the first approach to unify these three objectives within food authenticity analysis. Leveraging statistical inversion and region-wise significance inference, the model attributes spectral responses to underlying chemical mechanisms and directly informs band selection for portable spectrometer design. Compared with conventional black-box models, the proposed framework delivers high accuracy, intrinsic interpretability, and practical engineering applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Recently, growing consumer awareness of food quality and sustainability has led to a rising demand for effective food authentication methods. Vibrational spectroscopy techniques have emerged as a promising tool for collecting large volumes of data to detect food adulteration. However, spectroscopic data pose significant challenges from a statistical viewpoint, highlighting the need for more sophisticated modeling strategies. To address these challenges, in this work we propose a latent variable model specifically tailored for food adulterant detection, while accommodating the features of spectral data. Our proposal offers greater granularity with respect to existing approaches, since it does not only identify adulterated samples but also estimates the level of adulteration, and detects the spectral regions most affected by the adulterant. Consequently, the methodology offers deeper insights, and could facilitate the development of portable and faster instruments for efficient data collection in food authenticity studies. The method is applied to both synthetic and real honey mid-infrared spectroscopy data, delivering precise estimates of the adulteration level and accurately identifying which portions of the spectra are most impacted by the adulterant.

Problem

• Develops a latent variable model for detecting food adulteration
• Estimates adulteration levels and identifies affected spectral regions
• Applies the method to honey spectroscopy data for precise analysis

Innovation

• Latent variable model tailored for spectral data
• Estimates adulteration level and identifies affected spectral regions
• Applied to honey mid-infrared spectroscopy for precise detection